Abstract
Capturing document images using digital cameras in uneven lighting conditions is challenging, leading to poorly captured images, which hinders the processing that follows, such as Optical Character Recognition (OCR). In this paper, we propose the use of exposure bracketing techniques to solve this problem. Instead of capturing one image, we used several images that were captured with different exposure settings and used the exposure bracketing technique to generate a high-quality image that incorporates useful information from each image. We found that this technique can enhance image quality and provides an effective way of improving OCR accuracy. Our contributions in this paper are two-fold: (1) a preprocessing chain that uses exposure bracketing techniques for document images is discussed, and an automatic registration method is proposed to find the geometric disparity between multiple document images, which lays the foundation for exposure bracketing; (2) several representative exposure bracketing algorithms are incorporated in the processing chain and their performances are evaluated and compared.
Highlights
Camera document images provide many possibilities for further processing (for example, OpticalCharacter Recognition (OCR)) if the captured image quality is good [1]
For any exposure fusion method, there are two questions to answer: the first is how to select good weights for each low dynamic range (LDR) image pixel and the second is how to blend the weighted pixels into the final result
In order to clearly compare the output High dynamic range (HDR) image with the input LDR images, we zoomed in on some regions to visually see whether the HDR image enhanced the image quality or not, and we found that the texts in and the background is more homogeneous
Summary
Camera document images provide many possibilities for further processing One difficult case where the captured image is far from ideal is when there is uneven lighting. In the uneven lighting environment, irradiance across the scene varies greatly, leading to over-exposed imaging areas and under-exposed imaging areas regardless of the set exposure time. These over-exposed and under-exposed areas carry less information than the same areas when they are well-exposed. Document images are captured with different exposure times show characteristics Figure shows such an example where three exposure images are generated for the different characteristics. Generate better selecting the best image or pixels from these three images
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
